使用混合模型和 MDFC 特征提取方法对棉花作物病害进行分类

IF 1.1 4区 农林科学 Q3 PLANT SCIENCES
Padma P. Nimbhore, Ritu Tiwari, Tanmoy Hazra, Mahendra Pratap Yadav
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引用次数: 0

摘要

基于修正深度模糊聚类(MDFC)的新型分类模型包括四个主要阶段。它们分别是预处理、分割、特征提取以及最后的检测和分类阶段。为了减少噪声和平滑棉花作物输入图像的边缘,首先使用双边滤波作为预处理方法。接着,建议使用改进的深度模糊聚类进行分割,从预处理图像中创建分割集合。然后对分割后的图像进行处理,利用增强型直方图方向梯度金字塔(PHOG)、局部方向三元模式(LDTP)和基于统计的特征来提取相关特征。为了更有效地对棉花作物病害进行检测和分类,本文提出了一种混合系统。在此,先对特征进行检测,然后在双向门控递归单元(Bi-GRU)模型中对提取的特征进行训练,以确定棉花作物是否受到感染。一旦检测到棉花作物染病,就会通过改进的递归神经网络(RNN)对疾病类型进行分类。与传统方法相比,所提出的模型在多个性能指标方面都得到了验证。基于 MDFC 的分类模型优于现有模型,在学习率为 90 时,特异性为 0.9687。相比之下,其他模型的特异性较低:Bi-GRU (0.8436)、RNN (0.8359)、CNN (0.8654)、LSTM (0.8769)、SVM (0.7983)、VGG16 (0.8619)、DCNN (0.8725)、BI-RNN + BI-LSTM (0.7869) 和 NN + CNN (0.85478)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of cotton crop disease using hybrid model and MDFC feature extraction method

A novel Modified Deep Fuzzy Clustering (MDFC) based classification model involves four major phases. They are preprocessing, segmentation, feature extraction and finally, detection and classification phase. To reduce noise and smooth the edges of the input image of the cotton crop, bilateral filtering is first used as a preprocessing approach. Next, a modified deep fuzzy clustering is suggested for the segmentation procedure that creates a collection of segments from the preprocessed image. The segmented image is then processed to extract relevant characteristics by using an enhanced Pyramid of Histogram Orientation Gradient (PHOG), Local Directional Ternary Pattern (LDTP), and statistical-based features. In order to detect and classify cotton crop diseases more effectively, this paper proposes a hybrid system. Here, the features are put through a detection phase, after which the extracted features are trained in the Bidirectional Gated Recurrent Unit (Bi-GRU) model to determine whether or not the cotton crop is infected. Once it is detected to be diseased, the type of disease is classified via an improved Recurrent Neural Network (RNN). In terms of several performance metrics, the proposed model is validated in comparison with the traditional approaches. The MDFC-based classification model outperforms existing models with a specificity of 0.9687 at a learning rate of 90. In contrast, other models achieve lower specificities: Bi-GRU (0.8436), RNN (0.8359), CNN (0.8654), LSTM (0.8769), SVM (0.7983), VGG16 (0.8619), DCNN (0.8725), BI-RNN + BI-LSTM (0.7869), and NN + CNN (0.85478).

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来源期刊
Journal of Phytopathology
Journal of Phytopathology 生物-植物科学
CiteScore
2.90
自引率
0.00%
发文量
88
审稿时长
4-8 weeks
期刊介绍: Journal of Phytopathology publishes original and review articles on all scientific aspects of applied phytopathology in agricultural and horticultural crops. Preference is given to contributions improving our understanding of the biotic and abiotic determinants of plant diseases, including epidemics and damage potential, as a basis for innovative disease management, modelling and forecasting. This includes practical aspects and the development of methods for disease diagnosis as well as infection bioassays. Studies at the population, organism, physiological, biochemical and molecular genetic level are welcome. The journal scope comprises the pathology and epidemiology of plant diseases caused by microbial pathogens, viruses and nematodes. Accepted papers should advance our conceptual knowledge of plant diseases, rather than presenting descriptive or screening data unrelated to phytopathological mechanisms or functions. Results from unrepeated experimental conditions or data with no or inappropriate statistical processing will not be considered. Authors are encouraged to look at past issues to ensure adherence to the standards of the journal.
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